Mohammad Ali Izadbakhsh; Reza Hajiabadi
Abstract
In the article, through the adaptive neuro-fuzzy inference system (ANFIS), a sensitivity analysis is conducted on the variables affecting the discharge capacity of the weir. To this end, the variables affecting the discharge capacity of labyrinth weirs are initially identified. Then, using these input ...
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In the article, through the adaptive neuro-fuzzy inference system (ANFIS), a sensitivity analysis is conducted on the variables affecting the discharge capacity of the weir. To this end, the variables affecting the discharge capacity of labyrinth weirs are initially identified. Then, using these input parameters, seven ANFIS models are developed for conducting the sensitivity analysis. After that, the most optimal membership function number for the ANFIS model is chosen. In other words, by conducting the trial and error process, the best number of the membership functions in terms of time and modeling accuracy are selected. Then, the sensitivity analysis is performed for the ANFIS models and the superior ANFIS model is chosen finally. The accuracy of the superior model in both the validation and testing artificial intelligence (AI) methods is in an acceptable range. For example, the scatter index (SI), correlation coefficient (R) and the Nash-Sutcliff efficiency coefficient (NSC) for the model in the testing mode are obtained 0.049, 0.964 and 0.924, respectively. It should be noticed that the outcomes of the sensitivity analysis show that the ratio of the weir head to the weir crest and the Froude number are introduced as the most effective input parameters. Eventually, a computer code is proposed to estimate the discharge capacity of labyrinth weirs by this model.
Mohammadali Izadbakhsh; Reza Hajiabadi
Abstract
In this paper, the discharge coefficient of weirs is simulated by the extreme learning machine (ELM). To this end, seven different ELM models are introduced by the input parameters. Also, the most optimal number of the neurons in the hidden layer is computed 7. Furthermore, different activation functions ...
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In this paper, the discharge coefficient of weirs is simulated by the extreme learning machine (ELM). To this end, seven different ELM models are introduced by the input parameters. Also, the most optimal number of the neurons in the hidden layer is computed 7. Furthermore, different activation functions of the ELM model are assessed and the sigmoid activation function is taken into account as the most optimal one. Besides, the seven defined ELM models are analyzed and the superior model is introduced. This model approximates the discharge capacity with better performance in comparison with the other ELM models. It should also be noted that the superior ELM model is in terms of the dimensionless factors including Fr, HT/P, Lc/W, A/w, w/P. For the superior ELM model, the R2, VAF and NSC are respectively estimated 0.897, 89.626 and 0.892. Furthermore, the MAE and RMSE statistical indices for the ELM model are respectively estimated 0.024 and 0.031. Also, the most effective input parameters for modeling the discharge capacity of labyrinth weirs using the ELM are detected through the conduction of a sensitivity analysis, meaning that the HT/P is identified as the most influenced input parameter. Lastly, an applicable equation for computing the discharge capacity of labyrinth weirs is suggested which can be used by hydraulic and environmental engineers.
